{"id":"W6964757343","doi":"10.3389/ffgc.2021.688835.s001","title":"Data_Sheet_1_Seasonality in Human Interest in Berry Plants Detection by Google Trends.DOCX","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Phenology; Berry; Arctic; The Internet; Subarctic climate; The arctic","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008113991,0.001575196,0.001003715,0.003706811,0.0006561711,0.002101393,0.00164426,0.0013192,0.1338595],"category_scores_gemma":[0.004743201,0.0006287147,0.001171561,0.005515321,0.0003951555,0.001642742,0.001838188,0.001264998,0.09385965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258604,"about_ca_system_score_gemma":0.001572002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04081323,"about_ca_topic_score_gemma":0.07544183,"domain_scores_codex":[0.999368,0.00007582573,0.0001111391,0.0001620665,0.0001655259,0.0001174358],"domain_scores_gemma":[0.9976706,0.0006798751,0.0003263844,0.0003924913,0.0007008287,0.0002298572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000341647,0.0000152348,0.001864537,0.0006788727,0.0000228852,0.00002404,0.00003595917,0.0002094421,0.00009154953,0.0003410093,0.9948429,0.001839402],"study_design_scores_gemma":[0.0003086232,0.0000301443,0.02574336,0.0004971034,0.00004216793,0.0001006253,0.0002567173,0.0008066384,0.000605633,0.001062834,0.9704874,0.00005877636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009551705,0.00001056859,0.00001864099,0.00002854095,0.00000825972,0.000007037228,0.9994326,0.000110331,0.0002886096],"genre_scores_gemma":[0.0004286336,0.00001869169,0.0001529851,0.00002931951,0.000005427399,0.00007578932,0.998614,0.00005296976,0.0006221998],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1338595,"threshold_uncertainty_score":0.4478047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07996620153723649,"score_gpt":0.2917302205922886,"score_spread":0.2117640190550521,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}